class_docstr: Base class for increasing the learning rate between two boundaries over a number
  of iterations
class_docstr_tok: ["Base","class","for","increasing","the","learning","rate","between","two","boundaries","over","a","number","of","iterations"]
docstr: Args:
      optimizer: wrapped optimizer.
      end_lr: the final learning rate.
      num_iter: the number of iterations over which the test occurs.
      last_epoch: the index of last epoch.
  Returns:
      None
docstr_tok: ["Args",":","optimizer",":","wrapped","optimizer",".","end_lr",":","the","final","learning","rate",".","num_iter",":","the","number","of","iterations","over","which","the","test","occurs",".","last_epoch",":","the","index","of","last","epoch",".","Returns",":","None"]
code: def __init__(
      self,
      optimizer: Optimizer,
      end_lr: float,
      num_iter: int,
      last_epoch: int = -1,
  ) -> None:
      """
      Args:
          optimizer: wrapped optimizer.
          end_lr: the final learning rate.
          num_iter: the number of iterations over which the test occurs.
          last_epoch: the index of last epoch.
      Returns:
          None
      """
      self.end_lr = end_lr
      self.num_iter = num_iter
      super().__init__(optimizer, last_epoch)
  
file_path: MONAI/monai/optimizers/lr_scheduler.py
imports: from __future__ import annotations
  import math
  from torch.optim import Optimizer
  from torch.optim.lr_scheduler import LambdaLR, _LRScheduler
label_desc: Use image node_id 1 to create a new _LRSchedulerMONAI object from inherited base classes: _LRScheduler with example: obj = _LRSchedulerMONAI(optimizer, end_lr, num_iter, last_epoch)